Abstract
Instance-based learning algorithms, such as nearest neighbor (NN) classifiers, require storing all training instances and consulting them when making predictions. One alternative to overcome these costs is to reduce the learning dataset by a pre-processing step. This work deals with prototype generation, where new data points are generated from the original dataset. Reduction can be achieved by retaining less instances in the most representative areas of the dataset, which are represented by prototypes. Here Growing Neural Gas Networks are employed for generating the prototype instances. Experimentally, NN classifiers using the reduced datasets were able to maintain close accuracy to that of NN classifiers using the whole dataset.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aha, D.W., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Mach. Learn. 6(1), 37–66 (1991)
Wu, X., Kumar, V., Quinlan, J.R., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G.J., Ng, A., Liu, B., Yu, P.S., Zhou, Z.-H., Steinbach, M., Hand, D.J., Steinberg, D.: Top 10 algorithms in data mining. Knowl. Inf. Syst. 14(1), 1–37 (2008)
Garcia, S., Derrac, J., Cano, J.R., Herrera, F.: Prototype selection for nearest neighbor classification: taxonomy and empirical study. IEEE Trans. Pattern Anal. Mach. Intell. 34(3), 417–435 (2012)
Triguero, I., Derrac, J., Garcia, S., Herrera, F.: A taxonomy and experimental study on prototype generation for nearest neighbor classification. IEEE Trans. Syst. Man, Cybern. Part C 42(1), 86–100 (2012)
Liu, H.: Instance selection and construction for data mining. Springer, USA (2010)
Fritzke, B.: Advances in Neural Information Processing Systems 7. A growing neural gas network learns topologies, pp. 625–632. MIT Press, Cambridge (1995)
Koplowitz, J., Brown, T.A.: On the relation of performance to editing in nearest neighbor rules. Pattern Recogn. 13(3), 251–255 (1981)
Mollineda, R.A., Ferri, F.J., Vidal, E.: A merge-based condensing strategy for multiple prototype classifiers. IEEE Trans. Syst. Man Cybern. Part B Cybern. 32(5), 662–668 (2002)
Sánchez, J.S.: High training set size reduction by space partitioning and prototype abstraction. Pattern Recogn. 37(7), 1561–1564 (2004)
Kohonen, T.: The self-organizing map. Proc. IEEE. 78(9), 1464–1480 (1990)
Holmström, J.: Growing neural gas: Experiments with GNG, GNG with Utility and Supervised GNG. Uppsala Masters Thesis in Computer Science, Uppsala University Department of Information Technology (2002)
Alcalá, J., Fernández, A., Luengo, J., Derrac, J., García, S., Sánchez, L., Herrera, F.: Keel data-mining software tool: Data set repository, integration of algorithms and experimental analysis framework. J. Multiple-Valued Logic Soft Comput. 17(2–3), 255–287 (2011)
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)
Acknowledgments
The authors would like to thank the Brazilian National Research Council (CNPq) and the So Paulo Research Foundation (FAPESP), Proc. 2011/18496-7 and 2012/22608-8, for financial support.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Dias, J., Quiles, M.G., Lorena, A.C. (2015). Using Growing Neural Gas in Prototype Generation for Nearest Neighbor Classifiers. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9490. Springer, Cham. https://doi.org/10.1007/978-3-319-26535-3_32
Download citation
DOI: https://doi.org/10.1007/978-3-319-26535-3_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26534-6
Online ISBN: 978-3-319-26535-3
eBook Packages: Computer ScienceComputer Science (R0)